Mean square displacement analysis of single-particle trajectories with localization error: Brownian motion in an isotropic medium

Polarization-resolved single-molecule tracking reveals strange dynamics of fluorescent tracers through a deep rubbery polymer network

Tracking the movement of fluorescent single-molecule (SM) tracers has provided several new insights into the local structure and dynamics in complex environments such as soft materials and biological systems. However, SM tracking (SMT) remains unreliable at molecular length scales, as the localization error (LE) of SM trajectories (∼30-50 nm) is considerably larger than the size of molecular tracers (∼1-2 nm). Thus, instances of tracer (im)mobility in heterogeneous media, which provide indicators for underlying anomalous-transport mechanisms, remain obscured within the realms of SMT. Since the translation of passive tracers in an isotropic media is associated with fast dipolar rotation, we propose that authentic pauses within the LE can be revealed by probing the hindrance of SM reorientational dynamics. Here, we demonstrate how polarization-resolved SMT (PR-SMT) can provide emission anisotropy at each super-localized position, thereby revealing the tumbling propensity of SMs during random walks. For rhodamine 6G tracers undergoing heterogeneous transport in a hydrated polyvinylpyrrolidone (PVP) network, analysis of PR-SMT trajectories enabled us to discern instances of genuine immobility and localized motion within the LE. Our investigations on 100 SMs in (plasticized) PVP films reveal a wide distribution of dwell times and pause frequencies, demonstrating that most probes intermittently experience complete translational and rotational immobilization. This indicates that tracers serendipitously encounter compact, rigid polymer cavities during transport, implying the existence of nanoscale glass-like domains sparsely distributed in a predominantly deep-rubbery polymer network far above the glass transition.

Improving spatial precision and field-of-view in wavelength-tagged single-particle tracking using spectroscopic single-molecule localization microscopy

Spectroscopic single-molecule localization microscopy (sSMLM) generates super-resolution images of single molecules while simultaneously capturing the spectra of their fluorescence emissions. However, sSMLM splits photons from single-molecule emissions into a spatial channel and a spectral channel, reducing both channels' precisions. It is also challenging in transmission grating-based sSMLM to achieve a large field-of-view (FOV) and avoid overlap between the spatial and spectral channels. The challenge in FOV has further significance in single-molecule tracking applications. In this work, we analyzed the correlation between the spatial and spectral channels in sSMLM to improve its spatial precision, and we developed a split-mirror assembly to enlarge its FOV. We demonstrate the benefits of these improvements by tracking quantum dots. We also show that we can reduce particle-identification ambiguity by tagging each particle with its unique spectral characteristics.

Glass-like characteristics of intracellular motion in human cells

The motion in the cytosol of microorganisms such as bacteria and yeast has been observed to undergo a dramatic slowing down upon cell energy depletion. These observations have been interpreted as the motion being "glassy," but whether this notion is useful also for active, motor-protein-driven transport in eukaryotic cells is less clear. Here, we use fluorescence microscopy of beads in human (HeLa) cells to probe the motion of membrane-surrounded structures that are carried along the cytoskeleton by motor proteins. Evaluating several hallmarks of glassy dynamics, we show that at short length scales, the motion is heterogeneous, is nonergodic, is well described by a model for the displacement distribution in glassy systems, and exhibits a decoupling of the exchange and persistence times. Overall, these results suggest that the short length scale behavior of objects that can be transported actively by motor proteins in human cells shares features with the motion in glassy systems.

DNA replication origins retain mobile licensing proteins

DNA replication in eukaryotes initiates at many origins distributed across each chromosome. Origins are bound by the origin recognition complex (ORC), which, with Cdc6 and Cdt1, recruits and loads the Mcm2-7 (MCM) helicase as an inactive double hexamer during G1 phase. The replisome assembles at the activated helicase in S phase. Although the outline of replisome assembly is understood, little is known about the dynamics of individual proteins on DNA and how these contribute to proper complex formation. Here we show, using single-molecule optical trapping and confocal microscopy, that yeast ORC is a mobile protein that diffuses rapidly along DNA. Origin recognition halts this search process. Recruitment of MCM molecules in an ORC- and Cdc6-dependent fashion results in slow-moving ORC-MCM intermediates and MCMs that rapidly scan the DNA. Following ATP hydrolysis, salt-stable loading of MCM single and double hexamers was seen, both of which exhibit salt-dependent mobility. Our results demonstrate that effective helicase loading relies on an interplay between protein diffusion and origin recognition, and suggest that MCM is stably loaded onto DNA in multiple forms.

A role for Dynlt3 in melanosome movement, distribution, acidity and transfer

Skin pigmentation is dependent on cellular processes including melanosome biogenesis, transport, maturation and transfer to keratinocytes. However, how the cells finely control these processes in space and time to ensure proper pigmentation remains unclear. Here, we show that a component of the cytoplasmic dynein complex, Dynlt3, is required for efficient melanosome transport, acidity and transfer. In Mus musculus melanocytes with decreased levels of Dynlt3, pigmented melanosomes undergo a more directional motion, leading to their peripheral location in the cell. Stage IV melanosomes are more acidic, but still heavily pigmented, resulting in a less efficient melanosome transfer. Finally, the level of Dynlt3 is dependent on β-catenin activity, revealing a function of the Wnt/β-catenin signalling pathway during melanocyte and skin pigmentation, by coupling the transport, positioning and acidity of melanosomes required for their transfer.

Antioxidant Behavior Affected by Polarity in the Olive Oil: Experimental and Molecular Simulation Investigations

Natural antioxidants are essential potential sources for protecting the oxidation of food oils. However, until now, the mechanisms are still not very clear, especially from the quantitatively theoretical level to analyze the antioxidant behavior. In this work, the micromechanisms of the antioxidant behavior affected by polarity in the olive oil were systematically investigated by experimental and computational methods. The results showed that the polarity of antioxidants decreased with the growth of the alkyl chains, which had multiple impacts on the effectiveness of antioxidants. The excessive polarity gap between the antioxidant and oil molecules would cause the antioxidant to be dispersed at the oil-air interface, which could enhance their antioxidant ability. Meanwhile, the antioxidants with longer alkyl chains had lower polarity and better dispersibility but decreased mobility. Hence, compared with other antioxidants, medium polarity antioxidants presented both good dispersion and relatively suitable migration, indicating that they would have an optimal antioxidant effect.

Covariance distributions in single particle tracking

Several recent experiments, including our own experiments in the fission yeast, Schizosaccharomyces pombe, have characterized the motions of gene loci within living nuclei by measuring the locus position over time, then proceeding to obtain the statistical properties of this motion. To address the question of whether a population of such single-particle tracks, obtained from many different cells, corresponds to a single mode of diffusion, we derive theoretical equations describing the probability distribution of the displacement covariance, assuming the displacement itself is a zero-mean multivariate Gaussian random variable. We also determine the corresponding theoretical means, variances, and third central moments. Bolstering the theory is good agreement between its predictions and the results obtained for various simulated and measured data sets, including simulated particle trajectories undergoing simple and anomalous diffusion, and the measured trajectories of an optically trapped bead in water, and in a viscoelastic polymer solution. We also show that, for sufficiently long tracks, each covariance distribution in all of these examples is well-described by a skew-normal distribution with mean, variance, and skewness given by the theory. However, for the experimentally measured motion of a gene locus in S. pombe, we find that the first two covariance distributions are wider than predicted, although the third and subsequent covariance distributions are well-described by theory. This observation suggests that the origin of the theory-experiment discrepancy in this case is associated with localization noise, which influences only the first two covariances. Thus, we hypothesized that the discrepancy is caused by locus-to-locus heterogeneity in the localization noise, of independent measurements of the same tagged site. Indeed, simulations implementing heterogeneous localization noise revealed that the excess covariance widths can be largely recreated on the basis of heterogeneous noise. Thus, we conclude that the motion of gene loci in fission yeast is consistent with a single mode of diffusion.

Principles and Applications of Single Particle Tracking in Cell Research

It is a tough challenge for many decades to decipher the complex relationships between cell behaviors and cellular physical properties. Single particle tracking (SPT) with high spatial and temporal resolution has been applied extensively in cell research to understand physicochemical properties of cells and their bio-functions by tracking endogenous or exogenous probes. This review describes the fundamental principles of SPT as well as its applications in intracellular mechanics, membrane dynamics, organelles distribution, and processes of internalization and transport. Finally, challenges and future directions of SPT are also discussed.

Single-particle tracking photoactivated localization microscopy of membrane proteins in living plant tissues

Super-resolution microscopy techniques have pushed the limit of optical imaging to unprecedented spatial resolutions. However, one of the frontiers in nanoscopy is its application to intact living organisms. Here we describe the implementation and application of super-resolution single-particle tracking photoactivated localization microscopy (sptPALM) to probe single-molecule dynamics of membrane proteins in live roots of the model plant Arabidopsis thaliana. We first discuss the advantages and limitations of sptPALM for studying the diffusion properties of membrane proteins and compare this to fluorescence recovery after photobleaching (FRAP) and fluorescence correlation spectroscopy (FCS). We describe the technical details for handling and imaging the samples for sptPALM, with a particular emphasis on the specificity of imaging plant cells, such as their thick cell walls or high degree of autofluorescence. We then provide a practical guide from data collection to image analyses. In particular, we introduce our sptPALM_viewer software and describe how to install and use it for analyzing sptPALM experiments. Finally, we report an R statistical analysis pipeline to analyze and compare sptPALM experiments. Altogether, this protocol should enable plant researchers to perform sptPALM using a benchmarked reproducible protocol. Routinely, the procedure takes 3-4 h of imaging followed by 3-4 d of image processing and data analysis.

Collective dynein transport of the nucleus by pulling on astral microtubules during Saccharomyces cerevisiae mitosis

Positioning the nucleus at the bud neck during Saccharomyces cerevisiae mitosis involves pulling forces of cytoplasmic dynein localized in the daughter cell. Although genetic analysis has revealed a complex network positioning the nucleus, quantification of the forces acting on the nucleus and the number of dyneins driving the process has remained difficult. To better understand the collective forces involved in nuclear positioning, we compare a model of dyneins-driven microtubule (MT) pulling, MT pushing, and cytoplasmic drag to experiments. During S. cerevisiae mitosis, MTs interacting with the cortex nucleated by the daughter spindle pole body (SPB) (SPB-D) are longer than the mother SPB (SPB-M), increasing further during spindle elongation in anaphase. Interphasic SPB mobility is effectively diffusive, while the mitotic mobility is directed. By optimizing a computational model of the mobility of the nucleus due to diffusion and MTs pushing at the cell membrane to experiment, we estimate the viscosity governing the drag force on nuclei during positioning. A force balance model of mitotic SPB mobility compared to experimental mobility suggests that even one or two dynein dimers are sufficient to move the nucleus in the bud neck. Using stochastic computer simulations of a budding cell, we find that punctate dynein localization can generate sufficient force to reel in the nucleus to the bud neck. Compared to uniform motor localization, puncta involve fewer motors suggesting a functional role for motor clustering. Stochastic simulations also suggest that a higher number of force generators than predicted by force balance may be required to ensure the robustness of spindle positioning.

CYK4 relaxes the bias in the off-axis motion by MKLP1 kinesin-6

Centralspindlin, a complex of the MKLP1 kinesin-6 and CYK4 GAP subunits, plays key roles in metazoan cytokinesis. CYK4-binding to the long neck region of MKLP1 restricts the configuration of the two MKLP1 motor domains in the centralspindlin. However, it is unclear how the CYK4-binding modulates the interaction of MKLP1 with a microtubule. Here, we performed three-dimensional nanometry of a microbead coated with multiple MKLP1 molecules on a freely suspended microtubule. We found that beads driven by dimeric MKLP1 exhibited persistently left-handed helical trajectories around the microtubule axis, indicating torque generation. By contrast, centralspindlin, like monomeric MKLP1, showed similarly left-handed but less persistent helical movement with occasional rightward movements. Analysis of the fluctuating helical movement indicated that the MKLP1 stochastically makes off-axis motions biased towards the protofilament on the left. CYK4-binding to the neck domains in MKLP1 enables more flexible off-axis motion of centralspindlin, which would help to avoid obstacles along crowded spindle microtubules.

Analysing the 3D movement of MKLP1 motors, Maruyama et al. find that dimeric C. elegans MKLP1 drives a left-handed helical motion around the microtubule with minimum protofilament switching to the right side whereas less persistent motions are driven by monomers or by heterotetramers with CYK4. These findings suggest how obstacles along crowded spindle microtubules may be avoided by CYK4 binding to MKLP1.

An Estimation Algorithm for General Linear Single Particle Tracking Models with Time-Varying Parameters

Single Particle Tracking (SPT) is a powerful class of methods for studying the dynamics of biomolecules inside living cells. The techniques reveal the trajectories of individual particles, with a resolution well below the diffraction limit of light, and from them the parameters defining the motion model, such as diffusion coefficients and confinement lengths. Most existing algorithms assume these parameters are constant throughout an experiment. However, it has been demonstrated that they often vary with time as the tracked particles move through different regions in the cell or as conditions inside the cell change in response to stimuli. In this work, we propose an estimation algorithm to determine time-varying parameters of systems that discretely switch between different linear models of motion with Gaussian noise statistics, covering dynamics such as diffusion, directed motion, and Ornstein-Uhlenbeck dynamics. Our algorithm consists of three stages. In the first stage, we use a sliding window approach, combined with Expectation Maximization (EM) to determine maximum likelihood estimates of the parameters as a function of time. These results are only used to roughly estimate the number of model switches that occur in the data to guide the selection of algorithm parameters in the second stage. In the second stage, we use Change Detection (CD) techniques to identify where the models switch, taking advantage of the off-line nature of the analysis of SPT data to create non-causal algorithms with better precision than a purely causal approach. Finally, we apply EM to each set of data between the change points to determine final parameter estimates. We demonstrate our approach using experimental data generated in the lab under controlled conditions.

Locomotion of the C60-based nanomachines on graphene surfaces

We provide a comprehensive computational characterization of surface motion of two types of nanomachines with four C₆₀ "wheels": a flexible chassis Nanocar and a rigid chassis Nanotruck. We study the nanocars' lateral and rotational diffusion as well as the wheels' rolling motion on two kinds of graphene substrates-flexible single-layer graphene which may form surface ripples and an ideally flat graphene monolayer. We find that the graphene surface ripples facilitate the translational diffusion of Nanocar and Nanotruck, but have little effect on their surface rotation or the rolling of their wheels. The latter two types of motion are strongly affected by the structure of the nanomachines instead. Surface diffusion of both nanomachines occurs preferentially via a sliding mechanism whereas the rolling of the "wheels" contributes little. The axial rotation of all "wheels" is uncorrelated.

Single-Molecule Dynamics at a Bacterial Replication Fork after Nutritional Downshift or Chemically Induced Block in Replication

Replication forks must respond to changes in nutrient conditions, especially in bacterial cells. By investigating the single-molecule dynamics of replicative helicase DnaC, DNA primase DnaG, and lagging-strand polymerase DnaE in the model bacterium Bacillus subtilis, we show that proteins react differently to stress conditions in response to transient replication blocks due to DNA damage, to inhibition of the replicative polymerase, or to downshift of serine availability. DnaG appears to be recruited to the forks by a diffusion and capture mechanism, becomes more statically associated after the arrest of polymerase, but binds less frequently after fork blocks due to DNA damage or to nutritional downshift. These results indicate that binding of the alarmone (p)ppGpp due to stringent response prevents DnaG from binding to forks rather than blocking bound primase. Dissimilar behavior of DnaG and DnaE suggests that both proteins are recruited independently to the forks rather than jointly. Turnover of all three proteins was increased during replication block after nutritional downshift, different from the situation due to DNA damage or polymerase inhibition, showing high plasticity of forks in response to different stress conditions. Forks persisted during all stress conditions, apparently ensuring rapid return to replication extension.IMPORTANCE All cells need to adjust DNA replication, which is achieved by a well-orchestrated multiprotein complex, in response to changes in physiological and environmental conditions. For replication forks, it is extremely challenging to meet with conditions where amino acids are rapidly depleted from cells, called the stringent response, to deal with the inhibition of one of the centrally involved proteins or with DNA modifications that arrest the progression of forks. By tracking helicase (DnaC), primase (DnaG), and polymerase (DnaE), central proteins of Bacillus subtilis replication forks, at a single molecule level in real time, we found that interactions of the three proteins with replication forks change in different manners under different stress conditions, revealing an intriguing plasticity of replication forks in dealing with replication obstacles. We have devised a new tool to determine rates of exchange between static movement (binding to a much larger complex) and free diffusion, showing that during stringent response, all proteins have highly increased exchange rates, slowing down overall replication, while inactivation of polymerase or replication roadblocks leaves forks largely intact, allowing rapid restart once obstacles are removed.

Performance of deep learning restoration methods for the extraction of particle dynamics in noisy microscopy image sequences

Particle tracking in living systems requires low light exposure and short exposure times to avoid phototoxicity and photobleaching and to fully capture particle motion with high-speed imaging. Low-excitation light comes at the expense of tracking accuracy. Image restoration methods based on deep learning dramatically improve the signal-to-noise ratio in low-exposure data sets, qualitatively improving the images. However, it is not clear whether images generated by these methods yield accurate quantitative measurements such as diffusion parameters in (single) particle tracking experiments. Here, we evaluate the performance of two popular deep learning denoising software packages for particle tracking, using synthetic data sets and movies of diffusing chromatin as biological examples. With synthetic data, both supervised and unsupervised deep learning restored particle motions with high accuracy in two-dimensional data sets, whereas artifacts were introduced by the denoisers in three-dimensional data sets. Experimentally, we found that, while both supervised and unsupervised approaches improved tracking results compared with the original noisy images, supervised learning generally outperformed the unsupervised approach. We find that nicer-looking image sequences are not synonymous with more precise tracking results and highlight that deep learning algorithms can produce deceiving artifacts with extremely noisy images. Finally, we address the challenge of selecting parameters to train convolutional neural networks by implementing a frugal Bayesian optimizer that rapidly explores multidimensional parameter spaces, identifying networks yielding optimal particle tracking accuracy. Our study provides quantitative outcome measures of image restoration using deep learning. We anticipate broad application of this approach to critically evaluate artificial intelligence solutions for quantitative microscopy.

Mesoscale Modeling and Single-Nucleosome Tracking Reveal Remodeling of Clutch Folding and Dynamics in Stem Cell Differentiation

Nucleosomes form heterogeneous groups in vivo, named clutches. Clutches are smaller and less dense in mouse embryonic stem cells (ESCs) compared to neural progenitor cells (NPCs). Using coarse-grained modeling of the pluripotency Pou5f1 gene, we show that the genome-wide clutch differences between ESCs and NPCs can be reproduced at a single gene locus. Larger clutch formation in NPCs is associated with changes in the compaction and internucleosome contact probability of the Pou5f1 fiber. Using single-molecule tracking (SMT), we further show that the core histone protein H2B is dynamic, and its local mobility relates to the structural features of the chromatin fiber. H2B is less stable and explores larger areas in ESCs compared to NPCs. The amount of linker histone H1 critically affects local H2B dynamics. Our results have important implications for how nucleosome organization and H2B dynamics contribute to regulate gene activity and cell identity.

Cation interstitial diffusion in lead telluride and cadmium telluride studied by means of neural network potential based molecular dynamics simulations

Using a recently developed approach to represent ab initio based force fields by a neural network potential, we perform molecular dynamics simulations of lead telluride and cadmium telluride crystals. In particular, we study the diffusion of a single cation interstitial in these two systems. Our simulations indicate that the interstitials migrate via two distinct mechanisms: through hops between interstitial sites and through exchanges with lattice atoms. We extract activation energies for both of these mechanisms and show how the temperature dependence of the total diffusion coefficient deviates from Arrhenius behaviour. The accuracy of the neural network approach is estimated by comparing the results for three different independently trained potentials.

Modeling the dynamic behaviors of the COPI vesicle formation regulators, the small GTPase Arf1 and its activating Sec7 guanine nucleotide exchange factor GBF1 on Golgi membranes

The components and subprocesses underlying the formation of COPI-coated vesicles at the Golgi are well understood. The coating cascade is initiated after the small GTPase Arf1 is activated by the Sec7 domain–containing guanine nucleotide exchange factor GBF1 (Golgi brefeldin A resistant guanine nucleotide exchange factor 1). This causes a conformational shift within Arf1 that facilitates stable association of Arf1 with the membrane, a process required for subsequent recruitment of the COPI coat. Although we have atomic-level knowledge of Arf1 activation by Sec7 domain–containing GEFs, our understanding of the biophysical processes regulating Arf1 and GBF1 dynamics is limited. We used fluorescence recovery after photobleaching data and kinetic Monte Carlo simulation to assess the behavior of Arf1 and GBF1 during COPI vesicle formation in live cells. Our analyses suggest that Arf1 and GBF1 associate with Golgi membranes independently, with an excess of GBF1 relative to Arf1. Furthermore, the GBF1-mediated Arf1 activation is much faster than GBF1 cycling on/off the membrane, suggesting that GBF1 is regulated by processes other than its interactions Arf1. Interestingly, modeling the behavior of the catalytically inactive GBF1/E794K mutant stabilized on the membrane is inconsistent with the formation of a stable complex between it and an endogenous Arf1 and suggests that GBF1/E794K is stabilized on the membrane independently of complex formation.

Mass-sensitive particle tracking to elucidate the membrane-associated MinDE reaction cycle

In spite of their great importance in biology, methods providing access to spontaneous molecular interactions with and on biological membranes have been sparse. The recent advent of mass photometry to quantify mass distributions of unlabeled biomolecules landing on surfaces raised hopes that this approach could be transferred to membranes. Here, by introducing a new interferometric scattering (iSCAT) image processing and analysis strategy adapted to diffusing particles, we enable mass-sensitive particle tracking (MSPT) of single unlabeled biomolecules on a supported lipid bilayer. We applied this approach to the highly nonlinear reaction cycles underlying MinDE protein self-organization. MSPT allowed us to determine the stoichiometry and turnover of individual membrane-bound MinD/MinDE protein complexes and to quantify their size-dependent diffusion. This study demonstrates the potential of MSPT to enhance our quantitative understanding of membrane-associated biological systems.

Impact of Feature Choice on Machine Learning Classification of Fractional Anomalous Diffusion

The growing interest in machine learning methods has raised the need for a careful study of their application to the experimental single-particle tracking data. In this paper, we present the differences in the classification of the fractional anomalous diffusion trajectories that arise from the selection of the features used in random forest and gradient boosting algorithms. Comparing two recently used sets of human-engineered attributes with a new one, which was tailor-made for the problem, we show the importance of a thoughtful choice of the features and parameters. We also analyse the influence of alterations of synthetic training data set on the classification results. The trained classifiers are tested on real trajectories of G proteins and their receptors on a plasma membrane.

A method for imaging single molecules at the plasma membrane of live cells within tissue slices

Recent advances in light microscopy allow individual biological macromolecules to be visualized in the plasma membrane and cytosol of live cells with nanometer precision and ∼10-ms time resolution. This allows new discoveries to be made because the location and kinetics of molecular interactions can be directly observed in situ without the inherent averaging of bulk measurements. To date, the majority of single-molecule imaging studies have been performed in either unicellular organisms or cultured, and often chemically fixed, mammalian cell lines. However, primary cell cultures and cell lines derived from multi-cellular organisms might exhibit different properties from cells in their native tissue environment, in particular regarding the structure and organization of the plasma membrane. Here, we describe a simple approach to image, localize, and track single fluorescently tagged membrane proteins in freshly prepared live tissue slices and demonstrate how this method can give information about the movement and localization of a G protein–coupled receptor in cardiac tissue slices. In principle, this experimental approach can be used to image the dynamics of single molecules at the plasma membrane of many different soft tissue samples and may be combined with other experimental techniques.

Effect of Pixelation on the Parameter Estimation of Single Molecule Trajectories

The advent of single molecule microscopy has revolutionized biological investigations by providing a powerful tool for the study of intercellular and intracellular trafficking processes of protein molecules which was not available before through conventional microscopy. In practice, pixelated detectors are used to acquire the images of fluorescently labeled objects moving in cellular environments. Then, the acquired fluorescence microscopy images contain the numbers of the photons detected in each pixel, during an exposure time interval. Moreover, instead of having the exact locations of detection of the photons, we only know the pixel areas in which the photons impact the detector. These challenges make the analysis of single molecule trajectories, from pixelated images, a complex problem. Here, we investigate the effect of pixelation on the parameter estimation of single molecule trajectories. In particular, we develop a stochastic framework to calculate the maximum likelihood estimates of the parameters of a stochastic differential equation that describes the motion of the molecule in living cells. We also calculate the Fisher information matrix for this parameter estimation problem. The analytical results are complicated through the fact that the observation process in a microscope prohibits the use of standard Kalman filter type approaches. The analytical framework presented here is illustrated with examples of low photon count scenarios for which we rely on Monte Carlo methods to compute the associated probability distributions.

Extracting Transition Rates in Particle Tracking Using Analytical Diffusion Distribution Analysis

Single-particle tracking is an important technique in the life sciences to understand the kinetics of biomolecules. The analysis of apparent diffusion coefficients in vivo, for example, enables researchers to determine whether biomolecules are moving alone, as part of a larger complex, or are bound to large cellular components such as the membrane or chromosomal DNA. A remaining challenge has been to retrieve quantitative kinetic models, especially for molecules that rapidly switch between different diffusional states. Here, we present analytical diffusion distribution analysis (anaDDA), a framework that allows for extracting transition rates from distributions of apparent diffusion coefficients calculated from short trajectories that feature less than 10 localizations per track. Under the assumption that the system is Markovian and diffusion is purely Brownian, we show that theoretically predicted distributions accurately match simulated distributions and that anaDDA outperforms existing methods to retrieve kinetics, especially in the fast regime of 0.1-10 transitions per imaging frame. AnaDDA does account for the effects of confinement and tracking window boundaries. Furthermore, we added the option to perform global fitting of data acquired at different frame times to allow complex models with multiple states to be fitted confidently. Previously, we have started to develop anaDDA to investigate the target search of CRISPR-Cas complexes. In this work, we have optimized the algorithms and reanalyzed experimental data of DNA polymerase I diffusing in live Escherichia coli. We found that long-lived DNA interaction by DNA polymerase are more abundant upon DNA damage, suggesting roles in DNA repair. We further revealed and quantified fast DNA probing interactions that last shorter than 10 ms. AnaDDA pushes the boundaries of the timescale of interactions that can be probed with single-particle tracking and is a mathematically rigorous framework that can be further expanded to extract detailed information about the behavior of biomolecules in living cells.

Heterogeneous Subcellular Origin of Exosome-Mimetic Nanovesicles Engineered from Cells

Cell-engineered nanovesicles (CNVs) are considered as an alternative to exosomes, because they can be produced efficiently on a large scale and have been successfully reported in several applied research studies. However, CNVs may originate from various organelles, i.e., some of them may cause adverse effects on recipient cells, and their origin has not yet been identified. In this study, we air-sprayed human embryonic kidney 293 (HEK293) cells into lipid-bilayer CNVs. To identify the subcellular origin of the CNVs, we prepared nine different HEK293 cell lines by transfection with organelle-specific fluorescent protein plasmids that target the plasma membrane, peroxisome, lysosome, early endosome, late endosome, nucleus, mitochondrion, Golgi apparatus, and endoplasmic reticulum. The origin of CNVs were identified by measuring fluorescence expressions for organelle-specific markers using fluorescence nanoparticle tracking analysis (NTA). In the results, we found that CNVs derived from the plasma membrane constituted the largest portion, but CNVs derived from the other organelles comprised a non-negligible portion as well. This information will be useful to guide advanced research on outer membrane vesicles and exosome-mimetic nanovesicles engineered from cells.

DARC, Glycophorin A, Band 3, and GLUT1 Diffusion in Erythrocytes: Insights into Membrane Complexes

Single-particle tracking offers a method to interrogate the organization of transmembrane proteins by measuring their mobilities within a cell's plasma membrane. Using this technique, the diffusion characteristics of the Duffy antigen (DARC), glycophorin A, band 3, and GLUT1 were compared under analogous conditions on intact human erythrocyte membranes. Microscopic diffusion coefficients revealed that the vast majority of all four transmembrane proteins exhibit very restricted movement but are not completely immobile. In fact, only 12% of GLUT1 resolved into a highly mobile subpopulation. Macroscopic diffusion coefficients and compartment sizes were also similar for all four proteins, with movements confined to the approximate dimensions of the "corrals" of the cortical spectrin cytoskeleton. Taken together, these data suggest that almost the entire populations of all four transmembrane proteins are immobilized by either the incorporation within large multiprotein complexes or entrapment within the protein network of the cortical spectrin cytoskeleton.

Single-molecule analysis of dynamics and interactions of the SecYEG translocon

Protein translocation and insertion into the bacterial cytoplasmic membrane are the essential processes mediated by the Sec machinery. The core machinery is composed of the membrane-embedded translocon SecYEG that interacts with the secretion-dedicated ATPase SecA and translating ribosomes. Despite the simplicity and the available structural insights on the system, diverse molecular mechanisms and functional dynamics have been proposed. Here, we employ total internal reflection fluorescence microscopy to study the oligomeric state and diffusion of SecYEG translocons in supported lipid bilayers at the single-molecule level. Silane-based coating ensured the mobility of lipids and reconstituted translocons within the bilayer. Brightness analysis suggested that approx. 70% of the translocons were monomeric. The translocons remained in a monomeric form upon ribosome binding, but partial oligomerization occurred in the presence of nucleotide-free SecA. Individual trajectories of SecYEG in the lipid bilayer revealed dynamic heterogeneity of diffusion, as translocons commonly switched between slow and fast mobility modes with corresponding diffusion coefficients of 0.03 and 0.7 µm² ·s^-1 . Interactions with SecA ATPase had a minor effect on the lateral mobility, while bound ribosome:nascent chain complexes substantially hindered the diffusion of single translocons. Notably, the mobility of the translocon:ribosome complexes was not affected by the solvent viscosity or macromolecular crowding modulated by Ficoll PM 70, so it was largely determined by interactions within the lipid bilayer and at the interface. We suggest that the complex mobility of SecYEG arises from the conformational dynamics of the translocon and protein:lipid interactions.

A Large-Scale Array of Ordered Graphene-Sandwiched Chambers for Quantitative Liquid-Phase Transmission Electron Microscopy

Liquid-phase transmission electron microscopy (TEM) offers a real-time microscopic observation of the nanometer scale for understanding the underlying mechanisms of the growth, etching, and interactions of colloidal nanoparticles. Despite such unique capability and potential application in diverse fields of analytical chemistry, liquid-phase TEM studies rely on information obtained from the limited number of observed events. In this work, a novel liquid cell with a large-scale array of highly ordered nanochambers is constructed by sandwiching an anodic aluminum oxide membrane between graphene sheets. TEM analysis of colloidal gold nanoparticles dispersed in the liquid is conducted, employing the fabricated nanochamber array, to demonstrate the potential of the nanochamber array in quantitative liquid-phase TEM. The independent TEM observations in the multiple nanochambers confirm that the monomer attachment and coalescence processes universally govern the overall growth of nanoparticles, although individual nanoparticles follow different growth trajectories.

Fast-tracking of single emitters in large volumes with nanometer precision

Multifocal plane microscopy allows for capturing images at different focal planes simultaneously. Using a proprietary prism which splits the emitted light into paths of different lengths, images at 8 different focal depths were obtained, covering a volume of 50x50x4 µm³. The position of single emitters was retrieved using a phasor-based approach across the different imaging planes, with better than 10 nm precision in the axial direction. We validated the accuracy of this approach by tracking fluorescent beads in 3D to calculate water viscosity. The fast acquisition rate (>100 fps) also enabled us to follow the capturing of 0.2 µm fluorescent beads into an optical trap.

Multispectral Nanoparticle Tracking Analysis for the Real-Time and Label-Free Characterization of Amyloid-β Self-Assembly In Vitro

The deposition of amyloid β (Aβ) plaques and fibrils in the brain parenchyma is a hallmark of Alzheimer's disease (AD), but a mechanistic understanding of the role Aβ plays in AD has remained unclear. One important reason could be the limitations of current tools to size and count Aβ fibrils in real time. Conventional techniques from molecular biology largely use ensemble averaging; some microscopy analyses have been reported but suffer from low throughput. Nanoparticle tracking analysis is an alternative approach developed in the past decade for sizing and counting particles according to their Brownian motion; however, it is limited in sensitivity to polydisperse solutions because it uses only one laser. More recently, multispectral nanoparticle tracking analysis (MNTA) was introduced to address this limitation; it uses three visible wavelengths to quantitate heterogeneous particle distributions. Here, we used MNTA as a label-free technique to characterize the in vitro kinetics of Aβ1-42 aggregation by measuring the size distributions of aggregates during self-assembly. Our results show that this technology can monitor the aggregation of 106-108 particles/mL with a temporal resolution between 15 and 30 min. We corroborated this method with the fluorescent Thioflavin-T assay and transmission electron microscopy (TEM), showing good agreement between the techniques (Pearson's r = 0.821, P < 0.0001). We also used fluorescent gating to examine the effect of ThT on the aggregate size distribution. Finally, the biological relevance was demonstrated via fibril modulation in the presence of a polyphenolic Aβ disruptor. In summary, this approach measures Aβ assembly similar to ensemble-type measurements but with per-fibril resolution.

Complex Chromatin Motions for DNA Repair

A number of studies across different model systems revealed that chromatin undergoes significant changes in dynamics in response to DNA damage. These include local motion changes at damage sites, increased nuclear exploration of both damaged and undamaged loci, and directed motions to new nuclear locations associated with certain repair pathways. These studies also revealed the need for new analytical methods to identify directed motions in a context of mixed trajectories, and the importance of investigating nuclear dynamics over different time scales to identify diffusion regimes. Here we provide an overview of the current understanding of this field, including imaging and analytical methods developed to investigate nuclear dynamics in different contexts. These dynamics are essential for genome integrity. Identifying the molecular mechanisms responsible for these movements is key to understanding how their misregulation contributes to cancer and other genome instability disorders.

Super-Resolution Microscopy and Single-Molecule Tracking Reveal Distinct Adaptive Dynamics of MreB and of Cell Wall-Synthesis Enzymes

The movement of filamentous, actin-like MreB and of enzymes synthesizing the bacterial cell wall has been proposed to be highly coordinated. We have investigated the motion of MreB and of RodA and PbpH cell wall synthesis enzymes at 500 ms and at 20 ms time scales, allowing us to compare the motion of entire MreB filaments as well as of single molecules with that of the two synthesis proteins. While all three proteins formed assemblies that move with very similar trajectory orientation and with similar velocities, their trajectory lengths differed considerably, with PbpH showing shortest and MreB longest trajectories. These experiments suggest different on/off rates for RodA and PbpH at the putative peptidoglycan-extending machinery (PGEM), and during interaction with MreB filaments. Single molecule tracking revealed distinct slow-moving and freely diffusing populations of PbpH and RodA, indicating that they change between free diffusion and slow motion, indicating a dynamic interaction with the PGEM complex. Dynamics of MreB molecules and the orientation and speed of filaments changed markedly after induction of salt stress, while there was little change for RodA and PbpH single molecule dynamics. During the stress adaptation phase, cells continued to grow and extended the cell wall, while MreB formed fewer and more static filaments. Our results show that cell wall synthesis during stress adaptation occurs in a mode involving adaptation of MreB dynamics, and indicate that Bacillus subtilis cell wall extension involves an interplay of enzymes with distinct binding kinetics to sites of active synthesis.

A Deep Look into the Dynamics of Saltwater Imbibition in a Calcite Nanochannel: Temperature Impacts Capillarity Regimes

This research concerns fundamentals of spontaneous transport of saltwater (1 mol·dm^-3 NaCl solution) in nanopores of calcium carbonates. A fully atomistic model was adopted to scrutinize the temperature dependence of flow regimes during solution transport under CaCO₃ nanoconfinement. The early time of capillary filling is inertia-dominated, and the solution penetrates with a near-planar meniscus at constant velocity. Following a transition period, the meniscus angle falls to a stabilized value, characterizing the capillary-viscous advancement in the calcite channel. At this stage, brine displacement follows a parabolic relationship consistent with the classical Lucas-Washburn (LW) theory. Approaching the slit outlet, the meniscus contact lines spread widely on the solid substrate and brine leaves the channel at a constant rate, in oppose to the LW law. The brine imbibition rate considerably increases at higher temperatures as a result of lower viscosity and greater tendency to form wetting layers on slit walls. We also pointed out a longer primary inertial regime and delayed onset of the viscous-capillary regime at higher temperatures. Throughout the whole span of capillary displacement, transport of sodium and chloride ions is tied to dynamics and diffusion of the water phase, even at the mineral interface. The results presented in this study are of broad implications in diverse science and technological applications.

Single-Particle Tracking with Scanning Non-Linear Microscopy

This study describes the adaptation of non-linear microscopy for single-particle tracking (SPT), a method commonly used in biology with single-photon fluorescence. Imaging moving objects with non-linear microscopy raises difficulties due to the scanning process of the acquisitions. The interest of the study is based on the balance between all the experimental parameters (objective, resolution, frame rate) which need to be optimized to record long trajectories with the best accuracy and frame rate. To evaluate the performance of the setup for SPT, several basic estimation methods are used and adapted to the new detection process. The covariance-based estimator (CVE) seems to be the best way to evaluate the diffusion coefficient from trajectories using the specific factors of motion blur and localization error.

Long-term effects of the proline-rich antimicrobial peptide Oncocin112 on the Escherichia coli translation machinery

Proline-rich antimicrobial peptides (PrAMPs) are cationic antimicrobial peptides unusual for their ability to penetrate bacterial membranes and kill cells without causing membrane permeabilization. Structural studies show that many such PrAMPs bind deep in the peptide exit channel of the ribosome, near the peptidyl transfer center. Biochemical studies of the particular synthetic PrAMP oncocin112 (Onc112) suggest that on reaching the cytoplasm, the peptide occupies its binding site prior to the transition from initiation to the elongation phase of translation, thus blocking further initiation events. We present a superresolution fluorescence microscopy study of the long-term effects of Onc112 on ribosome, elongation factor-Tu (EF-Tu), and DNA spatial distributions and diffusive properties in intact Escherichia coli cells. The new data corroborate earlier mechanistic inferences from studies in vitro Comparisons with the diffusive behavior induced by the ribosome-binding antibiotics chloramphenicol and kasugamycin show how the specific location of each agent's ribosomal binding site affects the long-term distribution of ribosomal species between 30S and 50S subunits versus 70S polysomes. Analysis of the single-step displacements from ribosome and EF-Tu diffusive trajectories before and after Onc112 treatment suggests that the act of codon testing of noncognate ternary complexes (TCs) at the ribosomal A-site enhances the dissociation rate of such TCs from their L7/L12 tethers. Testing and rejection of noncognate TCs on a sub-ms timescale is essential to enable incorporation of the rare cognate amino acids into the growing peptide chain at a rate of ∼20 aa/s.

Live-Cell Imaging and Analysis of Nuclear Body Mobility

The cell nucleus contains different domains and nuclear bodies, whose position relative to each other inside the nucleus can vary depending on the physiological state of the cell. Changes in the three-dimensional organization are associated with the mobility of individual components of the nucleus. In this chapter, we present a protocol for live-cell imaging and analysis of nuclear body mobility. Unlike other similar protocols, our image analysis pipeline includes non-rigid compensation for global motion of the nucleus before particle tracking and trajectory analysis, leading to precise detection of intranuclear movements. The protocol described can be easily adapted to work with most cell lines and nuclear bodies.

Optimal estimates of self-diffusion coefficients from molecular dynamics simulations

Translational diffusion coefficients are routinely estimated from molecular dynamics simulations. Linear fits to mean squared displacement (MSD) curves have become the de facto standard, from simple liquids to complex biomacromolecules. Nonlinearities in MSD curves at short times are handled with a wide variety of ad hoc practices, such as partial and piece-wise fitting of the data. Here, we present a rigorous framework to obtain reliable estimates of the self-diffusion coefficient and its statistical uncertainty. We also assess in a quantitative manner if the observed dynamics is, indeed, diffusive. By accounting for correlations between MSD values at different times, we reduce the statistical uncertainty of the estimator and, thereby, increase its efficiency. With a Kolmogorov-Smirnov test, we check for possible anomalous diffusion. We provide an easy-to-use Python data analysis script for the estimation of self-diffusion coefficients. As an illustration, we apply the formalism to molecular dynamics simulation data of pure TIP4P-D water and a single ubiquitin protein. In another paper [S. von Bülow, J. T. Bullerjahn, and G. Hummer, J. Chem. Phys. 153, 021101 (2020)], we demonstrate its ability to recognize deviations from regular diffusion caused by systematic errors in a common trajectory "unwrapping" scheme that is implemented in popular simulation and visualization software.

Mucus Architecture and Near-Surface Swimming Affect Distinct Salmonella Typhimurium Infection Patterns along the Murine Intestinal Tract

Mucus separates gut-luminal microbes from the tissue. It is unclear how pathogens like Salmonella Typhimurium (S.Tm) can overcome this obstacle. Using live microscopy, we monitored S.Tm interactions with native murine gut explants and studied how mucus affects the infection. A dense inner mucus layer covers the distal colon tissue, limiting direct tissue access. S.Tm performs near-surface swimming on this mucus layer, which allows probing for colon mucus heterogeneities, but can also entrap the bacterium in the dense inner colon mucus layer. In the cecum, dense mucus fills only the bottom of the intestinal crypts, leaving the epithelium between crypts unshielded and prone to access by motile and non-motile bacteria alike. This explains why the cecum is highly infection permissive and represents the primary site of S.Tm enterocolitis in the streptomycin mouse model. Our findings highlight the importance of mucus in intestinal defense and homeostasis.

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Live imaging of Salmonella near-surface swimming on mouse colon inner mucus layer

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Colon inner mucus layer traversal requires mucus breaches and flagellar propulsion

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The mouse cecum lacks a continuous mucus layer, leaving epithelium tips uncovered

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Exposed cecum epithelium tips are a hotspot for Salmonella infection

PunctaSpecks: A tool for automated detection, tracking, and analysis of multiple types of fluorescently labeled biomolecules

Recent advances in imaging technology and fluorescent probes have made it possible to gain information about the dynamics of subcellular processes at unprecedented spatiotemporal scales. Unfortunately, a lack of automated tools to efficiently process the resulting imaging data encoding fine details of the biological processes remains a major bottleneck in utilizing the full potential of these powerful experimental techniques. Here we present a computational tool, called PunctaSpecks, that can characterize fluorescence signals arising from a wide range of biological molecules under normal and pathological conditions. Among other things, the program can calculate the number, areas, life-times, and amplitudes of fluorescence signals arising from multiple sources, track diffusing fluorescence sources like moving mitochondria, and determine the overlap probability of two processes or organelles imaged using indicator dyes of different colors. We have tested PunctaSpecks on synthetic time-lapse movies containing mobile fluorescence objects of various sizes, mimicking the activity of biomolecules. The robustness of the software is tested by varying the level of noise along with random but known pattern of appearing, disappearing, and movement of these objects. Next, we use PunctaSpecks to characterize protein-protein interaction involved in store-operated Ca2+ entry through the formation and activation of plasma membrane-bound ORAI1 channel and endoplasmic reticulum membrane-bound stromal interaction molecule (STIM), the evolution of inositol 1,4,5-trisphosphate (IP3)-induced Ca2+ signals from sub-micrometer size local events into global waves in human cortical neurons, and the activity of Alzheimer's disease-associated β amyloid pores in the plasma membrane. The tool can also be used to study other dynamical processes imaged through fluorescence molecules. The open source algorithm allows for extending the program to analyze more than two types of biomolecules visualized using markers of different colors.

Two-Parameter Mobility Assessments Discriminate Diverse Regulatory Factor Behaviors in Chromatin

Enzymatic probes of chromatin structure reveal accessible versus inaccessible chromatin states, while super-resolution microscopy reveals a continuum of chromatin compaction states. Characterizing histone H2B movements by single-molecule tracking (SMT), we resolved chromatin domains ranging from low to high mobility and displaying different subnuclear localizations patterns. Heterochromatin constituents correlated with the lowest mobility chromatin, whereas transcription factors varied widely with regard to their respective mobility with low- or high-mobility chromatin. Pioneer transcription factors, which bind nucleosomes, can access the low-mobility chromatin domains, whereas weak or non-nucleosome binding factors are excluded from the domains and enriched in higher mobility domains. Nonspecific DNA and nucleosome binding accounted for most of the low mobility of strong nucleosome interactor FOXA1. Our analysis shows how the parameters of the mobility of chromatin-bound factors, but not their diffusion behaviors or SMT-residence times within chromatin, distinguish functional characteristics of different chromatin-interacting proteins.

Single-molecule optical microscopy of protein dynamics and computational analysis of images to determine cell structure development in differentiating Bacillus subtilis

Here we use singe-molecule optical proteomics and computational analysis of live cell bacterial images, using millisecond super-resolved tracking and quantification of fluorescently labelled protein SpoIIE in single live Bacillus subtilis bacteria to understand its crucial role in cell development. Asymmetric cell division during sporulation in Bacillus subtilis presents a model system for studying cell development. SpoIIE is a key integral membrane protein phosphatase that couples morphological development to differential gene expression. However, the basic mechanisms behind its operation remain unclear due to limitations of traditional tools and technologies. We instead used advanced single-molecule imaging of fluorescently tagged SpoIIE in real time on living cells to reveal vital changes to the patterns of expression, localization, mobility and stoichiometry as cells undergo asymmetric cell division then engulfment of the smaller forespore by the larger mother cell. We find, unexpectedly, that SpoIIE forms tetramers capable of cell- and stage-dependent clustering, its copy number rising to ~ 700 molecules as sporulation progresses. We observed that slow moving SpoIIE clusters initially located at septa are released as mobile clusters at the forespore pole as phosphatase activity is manifested and compartment-specific RNA polymerase sigma factor, σF, becomes active. Our findings reveal that information captured in its quaternary organization enables one protein to perform multiple functions, extending an important paradigm for regulatory proteins in cells. Our findings more generally demonstrate the utility of rapid live cell single-molecule optical proteomics for enabling mechanistic insight into the complex processes of cell development during the cell cycle.

A Bayesian framework for the detection of diffusive heterogeneity

Cells are crowded and spatially heterogeneous, complicating the transport of organelles, proteins and other substrates. One aspect of this complex physical environment, the mobility of passively transported substrates, can be quantitatively characterized by the diffusion coefficient: a descriptor of how rapidly substrates will diffuse in the cell, dependent on their size and effective local viscosity. The spatial dependence of diffusivity is challenging to quantitatively characterize, because temporally and spatially finite observations offer limited information about a spatially varying stochastic process. We present a Bayesian framework that estimates diffusion coefficients from single particle trajectories, and predicts our ability to distinguish differences in diffusion coefficient estimates, conditional on how much they differ and the amount of data collected. This framework is packaged into a public software repository, including a tutorial Jupyter notebook demonstrating implementation of our method for diffusivity estimation, analysis of sources of uncertainty estimation, and visualization of all results. This estimation and uncertainty analysis allows our framework to be used as a guide in experimental design of diffusivity assays.

Estimation of general time-varying single particle tracking linear models using local likelihood

In this work, we study a general approach to the estimation of single particle tracking models with time-varying parameters. The main idea is to use local Maximum Likelihood (ML), applying a sliding window over the data and estimating the model parameters in each window. We combine local ML with Expectation Maximization to iteratively find the ML estimate in each window, an approach that is amenable to generalization to nonlinear models. Results using controlled-experimental data generated in our lab show that our proposed algorithm is able to track changes in the parameters as they evolve during a trajectory under real-world experimental conditions, outperforming other algorithms of similar nature.

Diffusive transport of nanoscale objects through cell membranes: a computational perspective

Diffusion is an essential and fundamental means of transport of substances on cell membranes, and the dynamics of biomembranes plays a crucial role in the regulation of numerous cellular processes. The understanding of the complex mechanisms and the nature of particle diffusion have a bearing on establishing guidelines for the design of efficient transport materials and unique therapeutic approaches. Herein, this review article highlights the most recent advances in investigating diffusion dynamics of nanoscale objects on biological membranes, focusing on the approaches of tailored computer simulations and theoretical analysis. Due to the presence of the complicated and heterogeneous environment on native cell membranes, the diffusive transport behaviors of nanoparticles exhibit unique and variable characteristics. The general aspects and basic theories of normal diffusion and anomalous diffusion have been introduced. In addition, the influence of a series of external and internal factors on the diffusion behaviors is discussed, including particle size, membrane curvature, particle-membrane interactions or particle-inclusion, and the crowding degree of membranes. Finally, we seek to identify open problems in the existing experimental, simulation, and theoretical research studies, and to propose challenges for future development.

Single-Molecule Super-Resolution Microscopy Reveals Heteromeric Complexes of MET and EGFR upon Ligand Activation

Receptor tyrosine kinases (RTKs) orchestrate cell motility and differentiation. Deregulated RTKs may promote cancer and are prime targets for specific inhibitors. Increasing evidence indicates that resistance to inhibitor treatment involves receptor cross-interactions circumventing inhibition of one RTK by activating alternative signaling pathways. Here, we used single-molecule super-resolution microscopy to simultaneously visualize single MET and epidermal growth factor receptor (EGFR) clusters in two cancer cell lines, HeLa and BT-20, in fixed and living cells. We found heteromeric receptor clusters of EGFR and MET in both cell types, promoted by ligand activation. Single-protein tracking experiments in living cells revealed that both MET and EGFR respond to their cognate as well as non-cognate ligands by slower diffusion. In summary, for the first time, we present static as well as dynamic evidence of the presence of heteromeric clusters of MET and EGFR on the cell membrane that correlates with the relative surface expression levels of the two receptors.

In Vivo Quantitative Imaging Provides Insights into Trunk Neural Crest Migration

Neural crest (NC) cells undergo extensive migrations during development. Here, we couple in vivo live imaging at high resolution with custom software tools to reveal dynamic migratory behavior in chick embryos. Trunk NC cells migrate as individuals with both stochastic and biased features as they move dorsoventrally to form peripheral ganglia. Their leading edge displays a prominent fan-shaped lamellipodium that reorients upon cell-cell contact. Computational analysis reveals that when the lamellipodium of one cell touches the body of another, the two cells undergo "contact attraction," often moving together and then separating via a pulling force exerted by lamellipodium. Targeted optical manipulation shows that cell interactions coupled with cell density generate a long-range biased random walk behavior, such that cells move from high to low density. In contrast to chain migration noted at other axial levels, the results show that individual trunk NC cells navigate the complex environment without tight coordination between neighbors.

Analysis and refinement of 2D single-particle tracking experiments

In recent decades, single particle tracking (SPT) has been developed into a sophisticated analytical approach involving complex instruments and data analysis schemes to extract information from time-resolved particle trajectories. Very often, mobility-related properties are extracted from these particle trajectories, as they often contain information about local interactions experienced by the particles while moving through the sample. This tutorial aims to provide a comprehensive overview about the accuracies that can be achieved when extracting mobility-related properties from 2D particle trajectories and how these accuracies depend on experimental parameters. Proper interpretation of SPT data requires an assessment of whether the obtained accuracies are sufficient to resolve the effect under investigation. This is demonstrated by calculating mean square displacement curves that show an apparent super- or subdiffusive behavior due to poor measurement statistics instead of the presence of true anomalous diffusion. Furthermore, the refinement of parameters involved in the design or analysis of SPT experiments is discussed and an approach is proposed in which square displacement distributions are inspected to evaluate the quality of SPT data and to extract information about the maximum distance over which particles should be tracked during the linking process.